Upload app.py

app.py

@@ -141,8 +141,7 @@ unique_PRIMARYMETHODPAYMENT = ['Private/commercial insurance', 'Medicaid', 'Medi
 y1 = x1.pop('OUTCOME')
 categorical_columns1 = list(x1.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
-for col in categorical_columns1:
-        x1[col] = le.fit_transform(x1[col].astype(str))
+x1[categorical_columns1] = x1[categorical_columns1].apply(le.fit_transform)
 d1 = dict.fromkeys(x1.select_dtypes(np.int64).columns, str)
 x1 = x1.astype(d1)
 
@@ -150,8 +149,7 @@ x1 = x1.astype(d1)
 y2 = x2.pop('OUTCOME')
 categorical_columns2 = list(x2.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
-for col in categorical_columns2:
-        x2[col] = le.fit_transform(x2[col].astype(str))
+x2[categorical_columns2] = x2[categorical_columns2].apply(le.fit_transform)
 d2 = dict.fromkeys(x2.select_dtypes(np.int64).columns, str)
 x2 = x2.astype(d2)
 
@@ -159,8 +157,7 @@ x2 = x2.astype(d2)
 y3 = x3.pop('OUTCOME')
 categorical_columns3 = list(x3.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
-for col in categorical_columns3:
-        x3[col] = le.fit_transform(x3[col].astype(str))
+x3[categorical_columns3] = x3[categorical_columns3].apply(le.fit_transform)
 d3 = dict.fromkeys(x3.select_dtypes(np.int64).columns, str)
 x3 = x3.astype(d3)
 
@@ -168,8 +165,7 @@ x3 = x3.astype(d3)
 y4 = x4.pop('OUTCOME')
 categorical_columns4 = list(x4.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
-for col in categorical_columns4:
-        x4[col] = le.fit_transform(x4[col].astype(str))
+x4[categorical_columns4] = x4[categorical_columns4].apply(le.fit_transform)
 d4 = dict.fromkeys(x4.select_dtypes(np.int64).columns, str)
 x4 = x4.astype(d4)
 
@@ -177,8 +173,7 @@ x4 = x4.astype(d4)
 y5 = x5.pop('OUTCOME')
 categorical_columns5 = list(x5.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
-for col in categorical_columns5:
-        x5[col] = le.fit_transform(x5[col].astype(str))
+x5[categorical_columns5] = x5[categorical_columns5].apply(le.fit_transform)
 d5 = dict.fromkeys(x5.select_dtypes(np.int64).columns, str)
 x5 = x5.astype(d5)